Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts

Abstract: We aim to evaluate the efficacy of traditional machine learning and LLMs in classifying anxiety and depression from long conversational transcripts. We fine-tune both established transformer models (BERT, RoBERTa, Longformer) and more recent large models (Mistral-7B), trained a Support Vector Machine with feature engineering, and assessed GPT models through prompting. We observe that state-of-the-art models fail to enhance classification outcomes compared to traditional machine learning methods.